Neural Network Application
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Olsoft Neural Network Library v.1.0
OLSOFT Neural Network Library is the class to create, learn and use Back Propagation neural networks and SOFM (Self-Organizing Feature Map). The library makes integration of neural networks' functionality into your own applications easy and seamless.
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Neural Network Component (ActiveX) v.1.0
Artificial Neural System Component is designed for researchers in the fields of machine learning, it can be used to construct Back Propagation Neural Network and to train it with provided samples, then finally recall it with appropriate data. This
Artificial Neural Network v.1.0
Create artificial neural networks. Artificial Neural Network demonstrate artificial intelligence. Taking advantage of serialization, there are two parts of the network. The actual network, and then training data.
Interactive Neural Network Simulator v.1.0
iSNS is an interactive neural network simulator written in Java/Java3D.
Java Neural Network Framework Neuroph v.2.5.1
Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures.
NeuroDiet v.3 4
NeuroDiet is a neural network application that learns how the foods you eat are related to your health and fitness. All the neural network functions are managed automatically. You don't need to know anything about neural networks.
EasyNN-plus
EasyNN-plus is a neural network system for Microsoft Windows. It makes the creation of neural networks easy. It allows the user to produce multilayer neural networks from a grid or from text files and images. The user can produce training, validating and
EasyNN v.8.0
EasyNN is a neural network system for Microsoft Windows. It makes the creation of neural networks easy. It allows the user to produce multilayer neural networks from a grid or from text files. The user can produce training,
Neural Creator v.1.0
Neural Creator is our visually based neural network development system that can be trained to perform pattern matching, feature recognition or make predictions on noisy or fuzzy data.
SwingNN
Your data is imported into a grid and used to train a neural network. The input values are forced to swing beyond their limits. The output values are forecasted by the neural network. A new neural network is created using the new inputs and forecasted